Results 221 to 230 of about 96,228 (258)

Greedy feature selection for ranking

Proceedings of the 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2011
This paper is concerned with a study on the feature selection for ranking. Learning to rank is a useful tool for collaborative filtering and many other collaborative systems, which many algorithms have been proposed for dealing this issue. But feature selection methods receive little attention, despite of their importance in collaborative filtering ...
Hanjiang Lai, Yong Tang 0001, Hai-Xia Luo, Yan Pan 0002   +3 more
openaire   +1 more source

Ensemble Feature Selection for Rankings of Features

2015
In the last few years, ensemble learning has been the focus of much attention mainly in classification tasks, based on the assumption that combining the output of multiple experts is better than the output of any single expert. This idea of ensemble learning can be adapted for feature selection, in which different feature selection algorithms act as ...
Borja Seijo-Pardo, Verónica Bolón-Canedo, Iago Porto-Díaz, Amparo Alonso-Betanzos   +3 more
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Feature Ranking for Protein Classification

2008
In this paper, a knowledge discovery framework is used for protein classification. The processing is achieved in three steps: feature extraction, feature ranking and feature selection. Inspirited from text mining results for the first step, we use n-grams descriptors; descriptors are ranked from chi-2 statistical indices in the second step; and in the ...
Faouzi Mhamdi, Ricco Rakotomalala, Mourad Elloumi   +2 more
openaire   +1 more source

Feature Ranking Computation Algorithm

International Journal of Organizational and Collective Intelligence, 2012
This journal paper describes an algorithm of feature ranking computation, based both on a data set with a potentially excessive number of features and a neural network trained and tested on this set. Each member of the data set contains many features (inputs) and one output.
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Fast Feature Selection for Learning to Rank

Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, 2016
An emerging research area named Learning-to-Rank (LtR) has shown that effective solutions to the ranking problem can leverage machine learning techniques applied to a large set of features capturing the relevance of a candidate document for the user query. Large-scale search systems must however answer user queries very fast, and the computation of the
Gigli A, Lucchese C, Nardini FM, Perego R   +3 more
openaire   +2 more sources

Social Ranking for Feature Selection

International Joint Conference on Autonomous Agents and Multiagent Systems
In this paper, we focus on limitations in the use of the Shapley value within the field of eXplainable AI (XAI) through the lens of the axiomatic analysis and its implications in the realm of machine learning. As an alternative to the Shapley value, we analyse the properties of the lex-cel, a social ranking solution introduced inthe recent literature ...
Laurent Gourvès, Stefano Moretti 0001, Satya Tamby   +2 more
openaire   +2 more sources

Learning to Rank with Labeled Features

Proceedings of the 2016 ACM International Conference on the Theory of Information Retrieval, 2016
Classic learning to rank algorithms are trained using a set of labeled documents, pairs of documents, or rankings of documents. Unfortunately, in many situations, gathering such labels requires significant overhead in terms of time and money. We present an algorithm for training a learning to rank model using a set of labeled features elicited from ...
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Ranking a random feature for variable and feature selection

J. Mach. Learn. Res., 2003
Summary: We describe a feature selection method that can be applied directly to models that are linear with respect to their parameters, and indirectly to others. It is independent of the target machine. It is closely related to classical statistical hypothesis tests, but it is more intuitive, hence more suitable for use by engineers who are not ...
Hervé Stoppiglia, Gérard Dreyfus, Rémi Dubois, Yacine Oussar   +3 more
openaire   +2 more sources

Ranked MSD: A New Feature Ranking and Feature Selection Approach for Biomarker Identification

2019
In the era of big data when a huge amount of data is continuously being generated, it is common for situations to arise where the number of samples is much smaller than the number of features (variables) per sample. This phenomenon is often found in biomedical domains, where we may have relatively few patients, compared to the amount of data per ...
Ghanshyam Verma, Alokkumar Jha, Dietrich Rebholz-Schuhmann, Michael G. Madden   +3 more
openaire   +1 more source

A Stratified Feature Ranking Method for Supervised Feature Selection

Proceedings of the AAAI Conference on Artificial Intelligence, 2018
Most feature selection methods usually select the highest rank features which may be highly correlated with each other. In this paper, we propose a Stratified Feature Ranking (SFR) method for supervised feature selection. In the new method, a Subspace Feature Clustering (SFC) is proposed to identify feature clusters, and a stratified ...
Renjie Chen 0004, Xiaojun Chen 0006, Guowen Yuan, Wenya Sun, Qingyao Wu   +4 more
openaire   +1 more source